Cold-Start Active Correlation Clustering
Abstract
We study active correlation clustering where pairwise similarities are not provided upfront and must be queried in a cost-efficient manner through active learning. Specifically, we focus on the cold-start scenario, where no true initial pairwise similarities are available for active learning. To address this challenge, we propose a coverage-aware method that encourages diversity early in the process. We demonstrate the effectiveness of our approach through several synthetic and real-world experiments.
Fields of Study & Subjects
Machine Learning and Algorithms
Advanced Clustering Algorithms Research
Complex Network Analysis Techniques
Publication details
- Journal / source
- ACM International Conference on Web Search and Data Mining (WSDM)
- Pages
- 1068–1072
- Publication date
- 16 February 2026
- Language
- EN